3d Structure Inspection or Metrology Using Deep Learning

2. The system of claim 1, wherein the one or more three-dimensional structures are one or more bumps formed on a wafer.

3. The system of claim 1, wherein determining the information comprises determining if any of the one or more three-dimensional structures are defective.

4. The system of claim 1, wherein the information comprises an average height metric for the one or more three-dimensional structures.

5. The system of claim 1, wherein the images input to the deep learning model are collected by the imaging subsystem in a single pass of the specimen.

6. The system of claim 1, wherein the images input to the deep learning model are collected by the imaging subsystem at a single focus value.

7. The system of claim 1, wherein the images comprise bright field images of the specimen or dark field images of the specimen.

8. The system of claim 1, wherein the images comprise bright field images of the specimen and dark field images of the specimen.

9. The system of claim 1, wherein the one or more computer systems are further configured for training the deep learning model with the images generated by the imaging subsystem of the specimen or a different specimen with two or more focus offsets.

10. The system of claim 1, wherein the one or more computer systems are further configured for training the deep learning model with images generated by the imaging subsystem of a different specimen having the three-dimensional structures formed thereon with multiple, known values of a characteristic of the three-dimensional structures.

11. The system of claim 1, wherein the one or more computer systems are further configured for locating and isolating one or more of the portions of the images corresponding to the one or more three-dimensional structures, respectively, and generating individual one or more cropped patch images for individual one or more three-dimensional structures, respectively, based on the isolated one or more portions.

12. The system of claim 1, wherein the one or more computer systems are further configured for said locating and isolating the portions of the images corresponding to the one or more three-dimensional structures by template matching.

13. The system of claim 1, wherein the one or more computer systems are further configured for said locating and isolating the portions of the images corresponding to the one or more three-dimensional structures based on design information for the specimen.

14. The system of claim 1, wherein the one or more computer systems are further configured for said locating and isolating the portions of the images corresponding to the one or more three-dimensional structures by inputting the images into a YOLO network configured for the locating and included in the one or more components executed by the one or more computer systems.

15. The system of claim 1, wherein the one or more computer systems are further configured for said locating and isolating the portions of the images corresponding to the one or more three-dimensional structures by inputting the images into an additional deep learning model configured for the locating and included in the one or more components executed by the one or more computer systems.

16. The system of claim 1, wherein the deep learning model is further configured as a convolutional neural network.

17. The system of claim 1, wherein the deep learning model comprises a combination of convolution layers and fully connected layers.

18. The system of claim 1, wherein the deep learning model is further configured as an AlexNet.

19. The system of claim 1, wherein the deep leaning model is further configured as a YOLO network, and wherein the YOLO network is further configured for said locating and isolating the portions of the images corresponding to the one or more three-dimensional structures.

20. The system of claim 1, wherein the imaging subsystem is further configured as an inspection subsystem.

21. The system of claim 1, wherein the imaging subsystem is further configured as a metrology subsystem.

22. The system of claim 1, wherein the imaging subsystem is further configured as a light based subsystem.

23. The system of claim 1, wherein the imaging subsystem is further configured as an electron beam subsystem.

24. The system of claim 1, wherein the specimen is a wafer.